Book Image

Practical Guide to Applied Conformal Prediction in Python

By : Valery Manokhin
4 (1)
Book Image

Practical Guide to Applied Conformal Prediction in Python

4 (1)
By: Valery Manokhin

Overview of this book

In the rapidly evolving landscape of machine learning, the ability to accurately quantify uncertainty is pivotal. The book addresses this need by offering an in-depth exploration of Conformal Prediction, a cutting-edge framework to manage uncertainty in various ML applications. Learn how Conformal Prediction excels in calibrating classification models, produces well-calibrated prediction intervals for regression, and resolves challenges in time series forecasting and imbalanced data. Discover specialised applications of conformal prediction in cutting-edge domains like computer vision and NLP. Each chapter delves into specific aspects, offering hands-on insights and best practices for enhancing prediction reliability. The book concludes with a focus on multi-class classification nuances, providing expert-level proficiency to seamlessly integrate Conformal Prediction into diverse industries. With practical examples in Python using real-world datasets, expert insights, and open-source library applications, you will gain a solid understanding of this modern framework for uncertainty quantification. By the end of this book, you will be able to master Conformal Prediction in Python with a blend of theory and practical application, enabling you to confidently apply this powerful framework to quantify uncertainty in diverse fields.
Table of Contents (19 chapters)
Free Chapter
Part 1: Introduction
Part 2: Conformal Prediction Framework
Part 3: Applications of Conformal Prediction
Part 4: Advanced Topics

Understanding classical predictors

Before we deep dive into the intricacies of conformal predictors, let’s briefly recap the key concepts from the previous chapters. Conformal prediction is a framework that enables creating confidence regions for our predictions while controlling the error rate.

This approach is especially beneficial in situations where a measure of uncertainty is essential, such as in medical diagnosis, self-driving cars, or financial risk management. The framework encompasses two main types of conformal predictors: classical and inductive.

Classical transductive conformal prediction (TCP) is the original form of conformal prediction developed by the inventors of Conformal prediction. It forms the basis for understanding the general principles of conformal predictors. Classical Conformal prediction was developed to construct prediction regions that conform to a specified confidence level. The critical aspect of classical Conformal prediction is its distribution...